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001 on1201526224;

003 OCoLC;

005 20210413024605.0;

006 m o d ;

007 cr unu||||||||;

008 201026s2020 ncua obm 000 0 eng d;

035 a| (Sirsi) o1201526224;

035 a| (OCoLC)1201526224;

049 a| EREE;

090 a| Q325.5;

100 1 a| Finch, Michael F.,e| author.?| UNAUTHORIZED;

245 10 a| Novelty detection for predictive maintenance /c| by Michael F. Finch.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2020.;

300 a| 49 pages :b| color illustrations;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 1.098 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| Computer Sciencec| East Carolina Universityd| 2020.;

500 a| Presented to the faculty of the Department of Computer Science.;

500 a| Advisor: M. N. H. Tabrizi;

500 a| Title from PDF t.p. (viewed April 9, 2021).;

520 3 a| Since the advent of Industry 4. 0 significant research has been conducted to apply machine learning to the vast array of Internet of Things (IoT) data produced by Industrial Machines. One such topic is to Predictive Maintenance. Unlike some other machine learning domains such as NLP and computer vision, Predictive Maintenance is a relatively new area of focus. Most of the published work demonstrates the effectiveness of supervised classification for predictive maintenance. Some of the challenges highlighted in the literature are the cost and difficulty of obtaining labelled samples for training. Novelty detection is a branch of machine learning that after being trained on normal operations detects if new data comes from the same process or is different, eliminating the requirement to label data points. This thesis applies novelty detection to both a public data set and one that was specifically collected to demonstrate a its application to predictive maintenance. The Local Optimization Factor showed better performance than a One-Class SVM on the public data. It was then applied to data from a 3-D printer and was able to detect faults it had not been trained on showing a slight lift from a random classifier.;

504 a| Includes bibliographical references.;

650 0 a| Machine learning.=| ^A145211;

650 0 a| Machineryx| Maintenance and repair.=| ^A156308;

650 0 a| Computer algorithms.=| ^A285321;

700 1 a| Tabrizi, M. N. H.,e| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/8736;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5524870;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5524870-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 10/26/2020x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5524870-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 10/26/2020x| ETDz| HERESOURCE;

Novelty detection for predictive maintenance / by Michael F. Finch.

Author/creator	Finch, Michael F. author.
Other author	Tabrizi, M. N. H., degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2020.
Description	49 pages : color illustrations
Supplemental Content	Access via ScholarShip
Subjects	Machine learning. Machinery--Maintenance and repair. Computer algorithms.

Summary	Since the advent of Industry 4. 0 significant research has been conducted to apply machine learning to the vast array of Internet of Things (IoT) data produced by Industrial Machines. One such topic is to Predictive Maintenance. Unlike some other machine learning domains such as NLP and computer vision, Predictive Maintenance is a relatively new area of focus. Most of the published work demonstrates the effectiveness of supervised classification for predictive maintenance. Some of the challenges highlighted in the literature are the cost and difficulty of obtaining labelled samples for training. Novelty detection is a branch of machine learning that after being trained on normal operations detects if new data comes from the same process or is different, eliminating the requirement to label data points. This thesis applies novelty detection to both a public data set and one that was specifically collected to demonstrate a its application to predictive maintenance. The Local Optimization Factor showed better performance than a One-Class SVM on the public data. It was then applied to data from a 3-D printer and was able to detect faults it had not been trained on showing a slight lift from a random classifier.
General note	Presented to the faculty of the Department of Computer Science.
General note	Advisor: M. N. H. Tabrizi
General note	Title from PDF t.p. (viewed April 9, 2021).
Dissertation note	Computer Science East Carolina University 2020.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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